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| Registros recuperados : 34 | |
| 21. |  | MISZTAL, I.; FRAGOMENI, B.; LOURENÇO, D. A. L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T. J. Efficient inversion of genomic relationship matrix by the Algorithm for Proven and Young (APY). Interbull Bulletin, 2015, v. 49, p. 111-116.| Biblioteca(s): INIA Las Brujas. |
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| 22. | | FRAGOMENI, B.O.; LOURENCO, D.A.L.; TSURUTA, S.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; LAWLOR, T.J.; MIZTAL, I. Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes. Journal of Dairy Science, 2015, v.98, no.6, p.4090-4094. OPEN ACCESS. Article history: Received November 18, 2014 / Accepted March 13, 2015 / Published online: April 8, 2015.| Biblioteca(s): INIA Las Brujas. |
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| 23. | | MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LEGARRA, A.; AGUILAR, I.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; LAWLOR, T.J. Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals. Journal of Dairy Science, 2016, v.99, no.3, p.1968-1974. OPEN ACCESS OPEN ACCESS. Received 19 October 2015, Accepted 1 December 2015, Available online 21 January 2016| Biblioteca(s): INIA Las Brujas. |
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| 24. | | MCWHORTER, T.M.; BERMANN, M.; GARCIA, A.L.S.; LEGARRA, A.; AGUILAR, I.; MISZTAL, I.; LOURENCO, D. Implication of the order of blending and tuning when computing the genomic relationship matrix in single-step GBLUP. Journal of Animal Breeding and Genetics, 2023, volume 140, issue 1, pp. 60-78. OPEN ACCESS. doi: https://doi.org/10.1111/jbg.12734 Article history: Received 18 March 2019; Revised 15 July 2019; Accepted: 29 July 2019; First published 10 August 2022.
Correspondence: McWhorter, T.M.; Department of Animal and Dairy Science, University of Georgia, Athens, GA, United...| Biblioteca(s): INIA Las Brujas. |
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| 25. | | LOURENCO, D.A.L.; MISZTAL, I.; TSURUTA, S.; AGUILAR, I.; EZRA, E.; RON, M.; SHIRAK, A.; WELLER, J.I. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science, 2014, v.97, no.3, p.1742-1752. OPEN ACCESS. Article history: Received September 10, 2013. / Accepted December 6, 2013.| Biblioteca(s): INIA Las Brujas. |
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| 26. | | LONDOÑO-GIL, M.; LÓPEZ-CORREA, R.; AGUILAR, I.; MAGNABOSCO, C.U.; HIDALGO, J.; BUSSIMAN, F.; BALDI, F.; LOURENCO, D. Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP. Journal of Animal Breeding and Genetics, 2024. https://doi.org/10.1111/jbg.12882 - [Early view] Article history: Received 22 March 2024, Revised 10 May 2024, Accepted 15 May 2024. -- Corresponding author: Londoño-Gil, M.; Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Via de...| Biblioteca(s): INIA Las Brujas. |
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| 27. | | LOURENCO, D.; TSURUTA, S.; FRAGOMENI, B.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; MILLER, S.; MOSER, D.; MISZTAL, I. Single-step genomic BLUP for national beef cattle evaluation in US: from initial developments to final implementation. Volume Species - Bovine (beef) 1, 495. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 11., Aotea Centre Auckland, New Zealand: WCGALP, ICAR, 11-16 feb 2018.| Biblioteca(s): INIA Las Brujas. |
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| 28. | | MASUDA, Y.; MISZTAL, I.; TSURUTA, S.; LOURENÇO, D. A. L.; FRAGOMENI, B.; LEGARRA, A.; AGUILAR, I.; LAWLOR, T. J. Single-step genomic evaluations with 570K genotyped animals in US Holsteins. Interbull Bulletin, 2015, v. 49, p. 85-89.| Biblioteca(s): INIA Las Brujas. |
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| 29. | | MASUDA, Y; MISZTAL, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.A.L.; FRAGOMENI, B.O.; AGUILAR, I. Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient. Journal of Animal Science, 2017, v. 95(1): 49-52. Article history: Received: July 05, 2016; Accepted: Aug 16, 2016; Published: February 2, 2017.
This research was partially funded by the United States Department of Agriculture?s National Institute of Food and Agriculture (Agriculture and...| Biblioteca(s): INIA Las Brujas. |
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| 30. | | LOURENCO, D. A. L.; TSURUTA, S.; FRAGOMENI, B. O.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; BERTRAND, J. K.; AMEN, T. S.; WANG. L.; MOSER, D. W.; MISZTAL, I. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*) Journal of Animal Science, 2015, v. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS. (*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s...| Biblioteca(s): INIA Las Brujas. |
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| 31. | | LOURENCO, D.A.L.; FRAGOMENI, B.O.; BRADFORD, H.L.; MENEZES I.R.; FERRAZ, J.B.S.; AGUILAR, I.; MISZTAL, I. Implications of SNP weighting on single-step genomic predictions for different reference population sizes. Journal of Animal Breeding and Genetics, 2017, v. 134 (6), p. 463-471. Article history: Received: 28 February 2017 / Accepted: 19 July 2017.
This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI) and by Agriculture and Food Research Initiative Competitive...| Biblioteca(s): INIA Las Brujas. |
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| 32. | | TONUSSI, R.L.; LONDOÑO-GIL, M.; DE OLIVEIRA SILVA, R.M.; MAGALHÃES, A.F.B.; AMORIM, S:T.; KLUSKA, S.; ESPIGOLAN, R.; PERIPOLLI, E.; PEREIRA, A.S.C.; LÔBO, R.B.; AGUILAR, I.; LOURENÇO, D.A.L.; BALDI, F. Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information. Tropical Animal Health and Production, 2021, Volume 53, Issue 4, Article number 432. doi: https://doi.org/10.1007/s11250-021-02879-w Article history: Received 19 March 2021; Accepted 30 July 2021; Published online 10 August 2021.
Corresponding author: Londoño-Gil, M.; Grupo de Melhoramento Animal, Faculdade de Ciências Agrárias E Veterinárias, Universidade Estadual...| Biblioteca(s): INIA Las Brujas. |
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| 33. | | MISZTAL, I.; WANG, H.; AGUILAR, I.; LEGARRA, A.; TSURUTA, S.; LOURENCO, D.; FRAGOMENI, B. O.; ZHANG, X.; MUIR, W. M.; CHENG, H. H.; OKIMOTO, R.; WING, T.; HAWKEN, R. R.; ZUMBACH, B.; FERNANDO, R. GWAS using ssGBLUP. Volume Species Breeding: Poultry, 325. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.325.| Biblioteca(s): INIA Las Brujas. |
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| 34. | | TONUSSI, R. L.; SILVA, R. M. D. O.; MAGALHÃES, A.F.B.; ESPIGOLAN, R.; PERIPOLLI, E.; OLIVIERI, B. F.; FEITOSA, F. L. B.; LEMOS, M. V. A.; BERTON, M. P.; CHIAIA, H. L. J.; PEREIRA, A. S. C.; LÔBO, R. B.; BEZERRA, L. A. F.; MAGNABOSCO, C. D. U.; LOURENÇO, D.A.L.; AGUILAR, I.; BALDI, F. Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. (Research article). PLoS ONE, September 2017, Volume 12, Issue 9, Article number e0181752. OPEN ACCESS. Article history: Received September 22, 2016 // Accepted July 6, 2017 // Published September 28, 2017.
Data Availability Statement: All relevant data are within the paper, its Supporting Information files, and in Figshare.
Funding: This...| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 34 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
26/11/2015 |
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Actualizado : |
29/01/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
LOURENCO, D. A. L.; TSURUTA, S.; FRAGOMENI, B. O.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; BERTRAND, J. K.; AMEN, T. S.; WANG. L.; MOSER, D. W.; MISZTAL, I. |
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Afiliación : |
D. A. L. LOURENCO, Universidad de Georgia (UG); SHOGO TSURUTA, Universidad de Georgia (UG); B. O. FRAGOMENI, Universidad de Georgia (UG); Y. MASUDA, Universidad de Georgia (UG); IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; A. LEGARRA, INRA (Institut National de la Recherche Agronomique); J. K. BERTRAND, Universidad de Georgia (UG); T. S. AMEN, Angus Genetics Inc.; L. WANG, Angus Genetics Inc.; D. W. MOSER, Angus Genetics Inc.; IGNACY MISZTAL, Universidad de Georgia (UG). |
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Título : |
Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*) |
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Fecha de publicación : |
2015 |
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Fuente / Imprenta : |
Journal of Animal Science, 2015, v. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS. |
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DOI : |
10.2527/jas.2014-8836 |
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Idioma : |
Inglés |
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Notas : |
(*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s National Institute of Food and Agriculture. We gratefully acknowledge the very helpful comments by the two anonymous reviewers. |
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Contenido : |
ABSTRACT.
Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals,
which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an
index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE?BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in
the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.
© 2015 American Society of Animal Science. All rights reserved MenosABSTRACT.
Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals,
which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an
index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE?BiW threshold-linear model. With BLUP, predictivities were 0.29,... Presentar Todo |
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Palabras claves : |
BEEF CATTLE; GENETIC RESURSION; INDIRECT PREDICTION. |
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Thesagro : |
GANADO DE CARNE; GENOMIC SELECTION; MEJORAMIENTO GENETICO ANIMAL. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5303/1/Lourenco-et-al-2015-JAS.pdf
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Marc : |
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024 7 $a10.2527/jas.2014-8836$2DOI
100 1 $aLOURENCO, D. A. L.
245 $aGenetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*)$h[electronic resource]
260 $c2015
500 $a(*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s National Institute of Food and Agriculture. We gratefully acknowledge the very helpful comments by the two anonymous reviewers.
520 $aABSTRACT. Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE?BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability. © 2015 American Society of Animal Science. All rights reserved
650 $aGANADO DE CARNE
650 $aGENOMIC SELECTION
650 $aMEJORAMIENTO GENETICO ANIMAL
653 $aBEEF CATTLE
653 $aGENETIC RESURSION
653 $aINDIRECT PREDICTION
700 1 $aTSURUTA, S.
700 1 $aFRAGOMENI, B. O.
700 1 $aMASUDA, Y.
700 1 $aAGUILAR, I.
700 1 $aLEGARRA, A.
700 1 $aBERTRAND, J. K.
700 1 $aAMEN, T. S.
700 1 $aWANG. L.
700 1 $aMOSER, D. W.
700 1 $aMISZTAL, I.
773 $tJournal of Animal Science, 2015$gv. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS.
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